Learning epipolar geometry from image sequences

Conference item

We wish to determine the epipolar geometry of a stereo camera pair from image measurements alone. This paper describes a solution to this problem, which does not require a parametric model of the camera system, and consequently applies equally well to a wide class of stereo configurations. Examples in the paper range from a standard pinhole stereo configuration to more exotic systems combining curved mirrors and wide-angle lenses. The method described here allows epipolar curves to be learnt from multiple image pairs acquired by stereo cameras with fixed configuration. By aggregating information over the multiple image pairs, a dense map of the epipolar curves can be determined on the images. The algorithm requires a large number of images, but has the distinct benefit that the correspondence problem does not have to be explicitly solved. We show that for standard stereo configurations the results are comparable to those obtained from a state of the art parametric model method, despite the significantly weaker constraints on the non-parametric model. The new algorithm is simple to implement, so it may easily be employed on a new and possibly complex camera system.

Authors: Wexler, Y; Fitzgibbon, AW; Zisserman, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings
• Pages: 209-216
• Publication date: 2003-07-15
• Event title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003)
• Event location: Madison, WI, USA
• Event website: https://www.cs.toronto.edu/cvpr2003/
• Event start date: 2003-06-18
• Event end date: 2003-06-20
• DOI: 10.1109/CVPR.2003.1211472
• ISSN: 1063-6919
• ISBN: 0-7695-1900-8
• Language: English